Brain magnetic resonance imaging is widely used as a diagnostic and monitoring tool in multiple sclerosis and provides a non-invasive, sensitive and reproducible way to track the disease. Topological characteristics relating to the distribution and shape of lesions are recognized as important neuroradiological markers in the diagnosis of multiple sclerosis, although these have been much less well characterized quantitatively than have traditional measures such as T2 hyperintense or T1 hypointense lesion volumes. Here, we used voxel-level 3 T magnetic resonance imaging T1-weighted scans to reconstruct the 3D topology of lesions in 284 subjects with multiple sclerosis and tested whether this is a heritable phenotype. To this end, we extracted the genotypes from a published genome-wide association study on these same individuals and searched for genetic associations with lesion load, shape and topological distribution. Lesion probability maps were created to identify frequently affected areas and to assess the overall distribution of T1 lesions in the subject population as a whole. We then developed an original algorithm to cluster adjacent lesional voxels (cluxels) in each subject and tested whether cluxel topology was significantly associated with any single-nucleotide polymorphism in our data set. To focus on patterns of lesion distribution, we computed the first 10 principal components. Although principal component 1 correlated with lesion load, none of the remaining orthogonal components correlated with any other known variable. We then conducted genome-wide association studies on each of these and found 31 significant associations (false discovery rate <0.01) with principal component 8, which represents a mode of variation of lesion topology in the population. The majority of the loci can be linked to genes related to immune cell function and to myelin and neural growth; some (SYK, MYT1L, TRAPPC9, SLITKR6 and RIC3) have been previously associated with the distribution of white matter lesions in multiple sclerosis. Finally, we used a bioinformatics approach to identify a network of 48 interacting proteins showing genetic associations (P < 0.01) with cluxel topology in multiple sclerosis. This network also contains proteins expressed in immune cells and is enriched in molecules expressed in the central nervous system that contribute to neural development and regeneration. Our results show how quantitative traits derived from brain magnetic resonance images of patients with multiple sclerosis can be used as dependent variables in a genome-wide association study. With the widespread availability of powerful computing and the availability of genotyped populations, integration of imaging and genetic data sets is likely to become a mainstream tool for understanding the complex biological processes of multiple sclerosis and other brain disorders.